Semantic Segmentation. Following [1]}, an UPerNet [2]} framework is used for ADE-20K semantic segmentation. We use an AdamW [3]} optimizer with a learning rate of 8e-5 for supervised model and a learning rate of 1e-4 for MIM model, a weight decay of 0.05 and a batch size of 32 for both models. Both models utilize a layer-wise learning rate decay of 0.95. All models are trained for 80K iterations with an input resolution of 640 \(\times \)  640 and a window size of 20. The drop path rate is set to 0.3 for supervised model and 0.1 for MIM model. In inference, a single-scale test using resolution of 2560 \(\times \)  640 is employed.
